{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Support Vector Machine with Randomized Search\n",
    "SVM is trained on feature set 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 3.00880939,  0.26443017,  1.05370334, ...\n",
      "X_32_val                  -> array([[-0.13964146,  0.53184264, -0.71694033, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[-0.1396414\n",
      "X_32train_std             -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[-0.006215\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 3.0088093\n",
      "X_train                   -> array([[[-0.01174874, -0.00817356, -0.0042913 , ..\n",
      "X_train_std               -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([3, 5, 1, ..., 2, 4, 0])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 3, ..\n",
      "y_32_train                -> array([5, 0, 2, ..., 4, 6, 6])\n",
      "y_32_val                  -> array([2, 2, 3, ..., 0, 3, 5])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([3, 5, 1, ..\n",
      "y_train                   -> array([5, 0, 2, ..., 4, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 32) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 32) arrays for SNR values:\n",
      "[-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_32train_std.shape, \"and labels: \", y_32_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_32test_std), X_32test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(sorted(X_32test_std.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 315.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randomized search took 324.00 minutes \n",
      "   \n",
      "Result of randomized search, best estimator:\n",
      "SVC(C=10.0, cache_size=5000, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "from sklearn import svm\n",
    "\n",
    "params = {'C': np.logspace(-3, 2, 6), 'gamma': np.logspace(-3, 2, 6)}\n",
    "\n",
    "rand_search_cv = RandomizedSearchCV(svm.SVC(cache_size = 5000), params, verbose=1)\n",
    "\n",
    "start = time()\n",
    "rand_search_cv.fit(X_train_std, y_train)\n",
    "print(\"Randomized search took %.2f minutes \"%((time() - start)//60))\n",
    "print(\"   \")\n",
    "print(\"Result of randomized search, best estimator:\")\n",
    "print(rand_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test the classifier\n",
      " \n",
      "SVM's accuracy on -20 dB SNR samples =  0.12675\n",
      "SVM's accuracy on -18 dB SNR samples =  0.12825\n",
      "SVM's accuracy on -16 dB SNR samples =  0.13525\n",
      "SVM's accuracy on -14 dB SNR samples =  0.1405\n",
      "SVM's accuracy on -12 dB SNR samples =  0.1525\n",
      "SVM's accuracy on -10 dB SNR samples =  0.18\n",
      "SVM's accuracy on -8 dB SNR samples =  0.2555\n",
      "SVM's accuracy on -6 dB SNR samples =  0.34375\n",
      "SVM's accuracy on -4 dB SNR samples =  0.417\n",
      "SVM's accuracy on -2 dB SNR samples =  0.42225\n",
      "SVM's accuracy on 0 dB SNR samples =  0.5275\n",
      "SVM's accuracy on 2 dB SNR samples =  0.67775\n",
      "SVM's accuracy on 4 dB SNR samples =  0.7585\n",
      "SVM's accuracy on 6 dB SNR samples =  0.76775\n",
      "SVM's accuracy on 8 dB SNR samples =  0.775\n",
      "SVM's accuracy on 10 dB SNR samples =  0.7715\n",
      "SVM's accuracy on 12 dB SNR samples =  0.77675\n",
      "SVM's accuracy on 14 dB SNR samples =  0.77625\n",
      "SVM's accuracy on 16 dB SNR samples =  0.768\n",
      "SVM's accuracy on 18 dB SNR samples =  0.769\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "\n",
    "print(\"Test the classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = rand_search_cv.predict(X_32test_std[snr])\n",
    "    accuracy[snr] = metrics.accuracy_score(y_32_test[snr], y_pred[snr])\n",
    "    print(\"SVM's accuracy on %d dB SNR samples = \"%(snr), accuracy[snr])   \n",
    "    \n",
    "accuracy = collections.OrderedDict(sorted(accuracy.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAsqLRUMmfFW2EkJ1KyTIZDJcv34dp0+fxq+//qpR4Nra2lZ5VzJ1j17FDQwMrHRCU4cO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9TURP/+/fHw4UO8efOmzscmCIKoCQpyCYIg5MDY2BgAkJGRUed9nZ2d8dVXX+H27du4f/++QupT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\nZGbUoWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f680a24c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Without Normalization\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK    360     2      2     9     2     58     64    68\n",
      "BPSK      0   492      0     0     4      4      8     3\n",
      "CPFSK    21     0    497    22     1      7      6    18\n",
      "GFSK     31     2      1   467     4      7      9    14\n",
      "PAM4      1     3      0     0   489      6      3     0\n",
      "QAM16    13     1      0     0     0    134    110    16\n",
      "QAM64     8     0      0     0     0    244    272    16\n",
      "QPSK     66     0      0     2     0     40     28   365\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "y_predicted = rand_search_cv.predict(X_32test_std[18])\n",
    "conf_matrix = confusion_matrix(y_predicted, y_32_test[18]) \n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix Without Normalization\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.64  0.00   0.00  0.02  0.00   0.10   0.11  0.12\n",
      "BPSK   0.00  0.96   0.00  0.00  0.01   0.01   0.02  0.01\n",
      "CPFSK  0.04  0.00   0.87  0.04  0.00   0.01   0.01  0.03\n",
      "GFSK   0.06  0.00   0.00  0.87  0.01   0.01   0.02  0.03\n",
      "PAM4   0.00  0.01   0.00  0.00  0.97   0.01   0.01  0.00\n",
      "QAM16  0.05  0.00   0.00  0.00  0.00   0.49   0.40  0.06\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.45   0.50  0.03\n",
      "QPSK   0.13  0.00   0.00  0.00  0.00   0.08   0.06  0.73\n"
     ]
    },
    {
     "data": {
      "image/png": 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/gDcrnGZmZj1YewNtKuVvRbVMyegHLC8EREnrA4cA0yPizgbXz8zMugFR5TzF\nFl0RvJbu0xuz18WS1iGtHtAXeFc2D+SKRlbQzMxaX09pKdYy+nR7oNAiHE56kPl+4AtA6yxsaWZm\nXUhV/deqe0fVEhTXBuZlf94PuD4ilpLmh2zUoHrlImmQpJ9JejLbVuRFSX+XdFJhrbts+5DlJa/n\nisrYRtINkmZnZcyU9NvsuSmSPpids03ROWtLul3SI1n3sZlZr1bVHMVqR6p2oVq6T58GPiHp/5FG\n+PwiSx9IFw60kbQxaQLnq8C3SfMk3yYtLnAiaaLmH4EgjZT9ddHpy7IyBpIWm/0DKcC/TgrshwBr\nkVZUICujcN33ADcBS4Ddi1ZiNzPrtXpK92ktQfFsYBxwMXBXRNydpe9DmkPSVX4FLAa2j4hFRemz\nSM88i70ZES+XKWM3YB3ghIhYnqU9y8ru4QIBSNoQuAX4N3BoRCyo6w7MzHqITlgQvCmq7j6NiN8C\nHyKtRL5P0aF7gG80qF7tkrQeaeGAX5YExGq9RPrF4LAO8gWwBXAXqUV6kAOimVnPU9N+ihHxXETc\nmz1LLKTdFRGPNK5q7dqU1Hp7ojhR0hxJb2Sv4oXJzytKny/pq1md/wGcA1wjaa6kP0s6NVvovE3R\npNbxk8Dh2Zp8ZmaWafTap81S036K2aCT4cAHgNWKj0XEkQ2oV612IAX6a0lrtBb8mGytvEzhWSER\ncYaki4C9gZ2Ak4D/kfSxiHi06JwbSPtzfQa4Lm+FvvWtUxkwYECbtM8OH8Hhh4/IW4SZ9TITJoxn\nwqQJbdLmzZtXIXeL6CEPFWuZvH8YMJ703G2P7OdmwLrAnxtau8qeInVpttloMiJmZXVcWJJ/bkQ8\nU6mwiHgN+B3wO0n/Q3o2eipQWEIoSM9SHwaulaSImJSnoueddwFDthuSJ6uZGQAjRoxkxIiRbdKm\nTbufnXfdqUk1yqET1j5thlq6T78HnBYR+5IGupxECoq/Bx5t78RGiYhXgb8AX5W0ZoPLXkoaYbtW\nUbKyYz8Evg/8RtLhjbyumVl3Vlj7NP+r2TUur5bu081YuV3UYmCtiFgq6XxSoDq7UZXrwFdIA1/+\nJelM4CHSnlk7kgbFdLg4uaSDgJGklu8TpOB3CHAgaTGCVUTEOZKWkZ5D9omI8fXfiplZ99ZDek9r\nCoqvsbIV9QKwJalbcW3gnQ2qV4ci4hlJQ0i7dpxD2lX5bdLWVj9m5arqUb4EyPK+BVxA2n7kbdJg\nmuMj4tpe03FzAAAgAElEQVTiy5Vc+zxJy4FxknBgNLPerjevfXo3aVDKI8D/A34m6WPAAcAdjata\nxyJiNnBy9qqUZ5N2js0kdf+2d41nSWu7lqb/mBR8zcx6vd7cUvxvoPAc7yxSl+WupEntYxpULzMz\n60aqXc20RWNiTfspvlz056WkgSdmZtabVbmiTas2FXONPpW0Wt5XZ1fYzMxaT2ctCC5pVLZRw0JJ\nUyTt0E7ePctsALGszIIsFeVtKS6i/QErxVZ5/mZmZj1bZ6x9KmkEcCFpk4f7gNHAZEkfjoi5FU4L\n4MPAGysSyq99XVbeoHhg3gLNzKz36aSBNqOByyJiXDpHJwEHAccB57dz3pyImJ+/NivlCooRMbmW\nws3MzGohqR9pU/tzCmkREZJuBXZp71TggWxP3UeA70fEPXmvW/WKNpKOkvTpMumHSTqi2vLMzKz7\nK8xTzP3qePzpQNLjuNkl6bOBwRXOeRH4EmmN6sNI2/zdIWm7vPdRy5SMMyg/t+810oT539ZQppmZ\ndWftdJ/e989buO9ft7ZJW7iw8XvSR8QTtN09aYqkD5G6YY/JU0YtQXEjYGaZ9JnAB2soz8zMurn2\nninutON+7LTjfm3Snn3ucX547rHlT0jmAsuAQSXpg0h74eZ1H2lD+VxqWRB8DrB1mfStgddrKM/M\nzLq56hYD73ikarZv7VRgWNE1lL3P/YwQ2I7UrZpLLS3FicDPJb0aEfcCSNoV+Fl2zMzMeplOGn16\nEXCVpKmsnJLRn2x/3Gwz+fUj4pjs/cmkXstHgTWAE4CPA/vmrVctQfF00s73dxftW7gGMAH4Tg3l\n9Wir9evL6qvXtJdzQ0XknWbauapa8aKTXffUKc2uwgrD+n2/2VVY4dbFrbFaY58+rfNvpRX+3fbp\nW0vHXtcR1X1OeXJGxERJA0lLig4i7XW7f0TMybIMJm3mULAaaV7j+sAC0u5JwyLib3nrVcsyb4uA\nT0n6CKlZuhB4KHvAaWZmvVEnLX4aEZewctej0mPHlryve6OGmpswEfEwacsoMzPr7XrI2qfN79cz\nM7NurzOWeWsGB0UzM6tbb95P0czMrI3CijbV5G9FDopmZla3ntJSrGmMr6QdJf1a0u2S1s/SRkra\nubHVMzMz6zq1LAh+CHAnsDpppfI1skPvBb7buKqZmVm3Ue1qNi3aVKylpTgG+GpEfB5YUpR+F2mb\nDzMz62VSnKsmMDa7xuXV8kxxC+C2MumvA+vWVx0zM+uOevMzxZeBjcuk70L53TPMzKyH64T9FJui\nlpbiWOCnko4GAni3pCHABcD5jaycmZl1D+ojVMV6tdXk7Uq1tBR/CPwBuBdYG5gCXAv8JiJ+0sC6\nlSVprKTlkpZJelvSk5LOkNSnJN8MSQslvbdMGXdkZZxW5tifsmPfq3D9S7PjX2vcXZmZdXNa2YWa\n59WiDcXqg2JELI+IM4D3AB8lbcsxOCK+2ejKteMm0urom5IWfx0DnFo4KGk30ujY64AvlDk/gOdK\nj2XTS/YGXih3UUmfBnYC/lNn/c3MepRG76fYLDXvRRIRb0XE/RHxt4h4rZGVyuHtiJgTEf+OiMuB\nW4FPFR0/nqz1ChxXoYw/AgMl7VKUdgwwmfTctA1J7yftGXkksLT+WzAz6znS1lFVvJpd4QqqfqYo\n6c/tHY+IT9RenZotAt4NIOmdwGeBHYAngAGSdouIu0vOWQxcQwqa92ZpXwC+CZxZnDHb7XkccH5E\nTG/V33DMzJqlpywIXktL8dmS1wukifu7Zu+7lKR9gP1ZOU1kJPBERMyIiOXAb0ktx3LGAodLWlPS\nHsA6pBZkqW8DiyPil42tvZlZz9Br5ylGxJfLpUs6h65rER8s6Q2gX3bNa1jZujuW1G1acC1wh6T/\njoi3iguJiIckPUFqWX4cGBcRy4t/g5G0PfA1YEgtFf3GqacwYMCANmkjR4xk5MgjainOzHqB8eN/\ny/gJ49ukzZs3r0m1yanaRWp6SlBsx1hSN+R3GlhmJX8FTiKtqPNC1iJE0pbAzsAOkoqnh/QhtSCv\nKFPWWGAUsCWpy7XU7qRBRf8uCpZ9gYskfT0iNmmvohdecBFDhw7Ne19mZowcecQqvzjff//97LhT\nua+oFtFDZu/XPNCmjKG0XfatM70VETMj4vlCQMwcT1qXdRtg26LXT6jchXot8BHg4Yh4vMzxcWXK\ne4E0J3P/BtyLmZlVIGmUpJnZFLspknL9ZiBpN0lLJN1fzfVqGWhzbWkS8D5gN5o4eV/SO4DPA9+N\niOklx34NnCJpy9JjEfG6pMFUCOjZyNo2o2slLQFeiognG3kPZmbdVWfspyhpBHAhcCJwHzAamCzp\nwxExt53zBgBXk2YmDMpdKWprKarktRx4APhMRJxeQ3mNcgiwHvD70gMRMQN4jAqtxYiYHxELi5M6\nuFZHx83MepWqpmPk72kdDVwWEeOy7/GTgAVUnmpXcClprMmUau+jqpaipL6krsjHI6IpT30j4tgK\n6deTBt5UOm/roj9/vINrtPsQsKPniGZmvU2jl3mT1I+089I5hbSICEm3ktbarnTesaT1uY8Czshd\noUxVLcWIWAb8nWxOoJmZGXRKS3EgaVDj7JL02aQVzcrUQZuRguhRJeNNcqtl9OljwIbAM7Vc0MzM\neqLKzxTvvPNP/O1vbdd9eeutNxp79bT+9TXAmIh4ekWlqlRLUDwNuEDSd4CpQOncv8U1lGlmZt1Y\nYfJ+OXvt9Un22uuTbdKeeupRvv714e0VORdYxqoDZQYBL5XJ/07SetzbSbo4S+tDWpRsMbBfRNzR\nwW3UFBQnl/ws1beGMs3MrBtr9DTFiFgiaSowjLQzU2HJzWHAz8ucMh/YuiRtFGlhls8As/LUq5ag\neGAN55iZWQ/WSWufXgRclQXHwpSM/sBVWRnnAutHxDEREaTHe8XXeBlYVDoVrz25g2K2v+AFEVGp\nhWhmZr1UZwTFiJgoaSBwFqnb9AFg/4iYk2UZTBrj0jDVjD4dQ9pU2MzMbBUNnqMIQERcEhEbRcSa\nEbFLRPyr6NixEbF3O+ee2dEUu1LVdJ+25kJ1ZmbWdD1l66hqnyl6JRczM1tFbw2KT0hqNzBGxHp1\n1MfMzKxpqg2KY4AW39TLzMy6Wg/ZOarqoDg+Il7ulJr0UEuWLGPx4qXNrgarrdbIrTOt0W5b8v1m\nV2GFq664r9lVAGDQ+gM6ztRF9t77Q82uAouXLGt2FdoldbyeaWn+VlTNN6WfJ5qZWXlVthRbdeim\nR5+amVndlP1XTf5WlDsoRkQtey+amVlvUNhht5r8LcgPmszMrG69dUqGmZnZKnrr6FMzM7NVqJ39\nFCvlb0UOimZmVr9eOPrUzMysLD9TNDMzy/iZopmZWSYFxe6/oo3nHpqZmWXcUjQzs7r1lO7Tlmkp\nStpA0pWS/iPpbUmzJP1U0ipbUUk6QtJSSb8oc2xPScslvSJptZJjH82OLStKW13SWEkPSVoi6foK\n9VtN0tlZvRZJekbSFxpw62Zm3Z5YGRhzvfKWK42SNFPSQklTJO3QTt7dJN0laa6kBZKmS/p6NffR\nEkFR0sbAv4APASOyn18ChgH3SnpXySnHAecBR5QGviJvAJ8uSTseeLYkrS+wAPgZ8Jd2qjkJ+Dhw\nLPBh4Ajg8Xbym5n1IqrqvzxhUdII4ELStoVDgAeByZIGVjjlLeAXwMeALYAfAD+U9MW8d9ESQRG4\nBHgb2Dci7oqI5yNiMrAP8H7g7ELGLIDuAvwIeBI4rEKZV5OCYOG8NYCRWfoKEbEgIkZFxBXA7HIF\nSTqA9CF/IiJuj4jnIuIfEXFvbbdrZtazVNVKzN/VOhq4LCLGRcQM4CRSI+a4cpkj4oGImBAR07Pv\n6WuByaTv71yaHhQlrQvsB1wcEYuLj0XEbOAaUuux4AvAnyLiDeA3QLnfAAL4P+BjkjbI0oYDM4Fp\nNVTzYFJL9luSnpf0uKQfZ4HWzKzXK8xTrObVQXn9gO2B2wppERHAraSGUZ46Dcny3pH3PpoeFIHN\nSO3oGRWOTwfWlTRQ6VP8AingAYwHdpP0wTLnvQzclOWH1O15ZY113IT0m8Z/AYcCJ5OC7MU1lmdm\n1qN0QktxIOnxVmkP3mxgcPt10b8lLQLuIzW4xua9j1YafdrRR7SY1KLsTwp2RMQrkm4lNaXHlDnn\nSuCnkq4BdiYFsj1qqFsfYDlwZES8CSDpFGCSpK9ExNuVTjztW6cyYEDbHcQP/+wIDj98RIUzzKy3\nmzhxApOum9Ambd68eU2qTT7ttf5uvvn3TJ58Q5u0N9+c35nV2R1Ym/S9f56kpyJiQgfnAK0RFJ8i\ndXduCdxQ5vhWwJyImC/peGA9YFHRhy/gI5QPijcBlwNXADdGxGs1Li30IvCfQkDMTM+uvQHwdKUT\nzz/vAoYMGVLLNc2slzr88FV/cZ72wDR2333nJtUon0pfrwceeCgHHnhom7Tp0x/mc587sL3i5gLL\ngEEl6YOAl9o7MSIKAyoflTQY+D6QKyg2vfs0Il4ljfr8iqTVi49lN3MkMDabmnEI6fnitkWvIaTu\n1f3KlL0MGAfsSQqMtbobWF9S/6K0zUmtx+frKNfMrEdo9DPFiFgCTCXNQihcQ9n7e6qoWl9g9Q5z\nZZoeFDNfJVV6sqSPZXMWDwBuIT1r/AFwNDA3Iq6LiMeKXg+TWoTFA26KP+3vAu+JiIrTLSRtKWk7\nUit0gKRtJW1blOVa4BVScN5S0h7A+cAV7XWdmpn1Gqrh1bGLgBMkHS1pC+BS0iO0qwAknStpxYwC\nSV+R9ElJm2av44FvsHIcSodaofuUiHgqm5D5fVIT972kgP074PMRsUjSsUDZifVZvnFFE/2jqOyl\nwKsdVOHPwAeK3k/LyuiblfGWpH1J81/+SQqQE4Az8t6jmVlP1hlrn0bExGxO4lmkbtMHgP0jYk6W\nZTCwYdEpfYBzgY2ApaRHW9+MiMvz1qslgiJARDxH0dwTSWOAU4BtgPsiYtt2zp1EmlwPcCdZMKuQ\n94bS4xGxcY76PQHs31E+M7PeqLOWeYuIS0hz2csdO7bk/S+BX+avxapaJiiWiogzJc0ijR66r8nV\nMTOzXqBlgyJARFzdcS4zM2s2UeUmw7lXP+1aLR0Uzcys+2jNMFcdB0UzM6tbnmkWpflbkYOimZnV\nrafsp+igaGZmdXNL0czMLOOWopmZWZFWDXTVcFA0M7O6ufvUzMws4+5TMzOzTGesfdoMDoqd7B39\n+tCvX8WlWM1aTv+1V2t2FQB48fnXm12FFd7Rt/kbCr2jT4tGkR7GQdHMzOrWU54pNv/XHzMzsxbh\nlqKZmTVEizb+quKWopmZWcYtRTMzq5unZJiZmWWU/VdN/lbk7lMzM6ufanjlKVYaJWmmpIWSpkja\noZ28n5Z0i6SXJc2TdI+k/aq5DQdFMzOrW6H7tJpXx2VqBHAhMAYYAjwITJY0sMIpewC3AAcCQ4Hb\ngRslbZv3PhwUzcysbqrhvxxGA5dFxLiImAGcBCwAjiuXOSJGR8QFETE1Ip6OiNOBJ4GD896Hg6KZ\nmdWvwd2nkvoB2wO3FdIiIoBbgV1yVSmtEPBO4NW8t+GgaGZmDdHgx4kDgb7A7JL02cDgnFX6JrAW\nMDFnfo8+NTOz+onKy7zdcMN13PCH37VJmz9/fufWRzoSOAM4JCLm5j2vZYKipA2As4D9Sb8hvAj8\nHjgrIl4tyXsE8H/AryLiv0uO7Ul6uPoa8L6IWFx07KPAfaRWeN+S804FTgA+CMwBLomIc8vUczfg\nDuDhiBhazz2bmfUY7TQBP3XocD516PA2aQ8//CAHHbRXeyXOBZYBg0rSBwEvtVsVaSRwOTA8Im5v\nL2+plug+lbQx8C/gQ8CI7OeXgGHAvZLeVXLKccB5wBGSKi3p/wbw6ZK044Fny1z/51mZpwCbA4eQ\ngmdpvgHA1aQ+bTMzyzR6RkZELAGmkuJAukZqig4D7qlYj9RougIYGRE3V3sfLREUgUuAt4F9I+Ku\niHg+IiYD+wDvB84uZMwC6C7Aj0ijig6rUObVpCBYOG8NYGSWTlH6lqQRTYdExJ8i4tmImBYRt7Gq\nS4FrgCm13aaZmVXhIuAESUdL2oL0HdwfuApA0rmSVnynZ12mVwPfAP4paVD2WifvBZseFCWtC+wH\nXFzc1QkQEbNJQWhEUfIXgD9FxBvAb4Avlik2SN2rH8u6ZQGGAzOBaSV5Pwk8DRwi6Zlskuj/ZvUq\nruexwMbAmdXfpZlZz1bYZDj/q+MyI2IicCrp0do0YBtg/4iYk2UZDGxYdMoJpME5FwMvFL1+mvc+\nWuGZ4maklvSMCsenA+tmkzVfIQXFUdmx8cAFkj4YEaXdoi8DN2X5fwgcC1xZpvxNgI1IQfNzpM/k\np8AkUksVSZsB5wC7R8TyavYBO/XUbzBgnQFt0kaMGMnIkSNzl2FmvcuECeOZMGlCm7R58+Y1qTbN\nFRGXkHoTyx07tuT9x+u9XisExYKOIs1iUouyPynYERGvSLqV9DxwTJlzrgR+KukaYGdS4NujJE8f\nYDXg8xHxNICk44GpWTB8mtRaHVM4nqOuK1xwwYUMHeLxOGaW34gRIxkxou0vztOm3c/Ou+7UpBp1\nrKcsCN707lPgKVJ355YVjm8FzImI+aRnhOsBiyQtkbSEtJzPMRXOvYkURK8AboyI18rkeRFYWhTw\nILVOAT5Amvj5UeCXRdc8A9hO0mJJe+W8TzOznquqrtMqI2gXanpQzKZb/AX4iqTVi49JGgwcCYyV\ntB5pVOgIYNui1xBS9+oqi75GxDJgHLAnKTCWczfwjmwAT8HmpED9LDAf2BrYruial5K6e7cF/lH9\nXZuZWStqle7Tr5KC02RJZ5AGxGwNnE8KPj8ATgTmRsR1pSdLuok04OaWQlLR4e8C55fOdSxyK3A/\ncKWk0aSHtL8EbomIp7I8j5Vc72VgUURMx8zM0jSLarpPO60m9Wl6SxEgCz47AM8AE4BZwJ+Bx0mD\nWxaQBspcX6GI3wEHZ61JSK28QtlL2wmIhbX0DiZNFL0TuBF4FDiijlsyM+tVOmlB8C7XKi1FIuI5\nilY+lzSGNJl+G+C+iKi49UdETCKNFoUU2Pq2k/eG0uMR8RLw2SrqeiaemmFmtlIVi5quyN+CWiYo\nloqIMyXNIo0aXWV1GTMzax09ZfRpywZFgIi4uuNcZmbWbD2kodjaQdHMzLqJHtJUdFA0M7OGaM0w\nV52WGH1qZmbWCtxSNDOzuvWQ3lMHRTMza4Rql25rzajooGhmZnXz6FMzM7OMu0/NzMzaaNFIVwUH\nxU721huLmT9vUbOrwYB3rdnsKlg3cfDBWzW7CgD06ds6g+O/fsRvm10F5rw+q9lVaFdPaSm2zr86\nMzOzJnNQNDOz+mllazHPK29Pq6RRkmZKWihpiqQd2sk7WNI1kh6XtEzSRdXehoOimZk1gGp4dVCi\nNAK4EBhD2lD+QdK+uwMrnLI68DJpD94HarkLB0UzM6tbNa3EKp4/jgYui4hxETEDOAlYQNE2g8Ui\n4tmIGB0RvwHm13IfDopmZtZyJPUDtgduK6Rlm8LfCuzSWdd1UDQzs8ZoXM8pwEDShvCzS9JnA4Mb\nUt8yPCXDzMw61XXXTeS66ya2SZs3f16TatM+B0UzM6ubsv/K+ezwEXx2+Ig2aQ88MI099tq1vSLn\nAsuAQSXpg4CXaq9p+9x9amZmLScilgBTgWGFNEnK3t/TWdd1S9HMzOrWSSvaXARcJWkqcB9pNGp/\n4KpUhs4F1o+IY1aWq21JTy3XBt6TvV8cEdPzXNBB0czMWlJETMzmJJ5F6jZ9ANg/IuZkWQYDG5ac\nNg2I7M9DgSOBZ4FN8lzTQdHMzOrXSU3FiLgEuKTCsWPLpNX1WLBbP1OUtIGkKyX9R9LbkmZJ+qmk\n9Yry3CFpefZaKOlRSV8uOt5H0rclTZe0QNIr2VJCxxXlGSvp+pJrD8/KG901d2tm1roav55Nc3Tb\noChpY+BfwIeAEdnPL5Eewt4r6V1Z1gAuJzW9twQmAhdLOjw7/n3gZOD07PhewGVA4fxy1/4i8H/A\nlyLiJ428LzOzbqmHRMXu3H16CfA2sG9ELM7Snpf0APA0cDYwKktfkPVBzwHOlHQE8ClSgDwYuCQi\niluCD1e6qKTTSOvwjYiIPzTyhszMurMWjXNV6ZYtRUnrAvsBFxcFRAAiYjZwDan1WMkiYLXszy8B\ne7ezwGzxdX9EalEe5IBoZlakkxY/7WrdMigCm5F+KZlR4fh0YN3SQJc9P/wc8BFWrqd3CvAe4CVJ\nD0r6laQDypT5CeCbwKci4o4G3IOZmbWY7tx9Ch231gutyFGSTiC1DpcCF0XEpQDZ3JWtJW0P7Abs\nAdwoaWxEnFhU1oOktfjOknRgRLyVp4Knn/EtBqwzoE3aYYcN5zOHHV7hDDPr7Z58/l6efH5Km7TF\nSxY0qTb5VPuYsDXbid03KD5FGkCzJXBDmeNbAXMiYn5aAIHfkJ4xLoyIF8sVGBFTSasn/FzSUcA4\nSWdHxLNZlv8Aw4E7gJslHZAnMJ79g/PYdtvtqro5M+vdNttgFzbboO1GEHNen8V1d3yvSTXKqVUj\nXRW6ZfdpRLwK/AX4iqTVi49JGkyarDm2KHleRDxTKSCWUVj5YK2S6/4b2JM0YXSypLVKTzQz641U\nw3+tqFsGxcxXSbssT5b0sWzO4gHALaRnjT/IU4ikSZK+LmlHSR+QtBfwS+BxyjyzjIjnSYHxvcAt\nkt7ZmNsxM7Nm67ZBMSKeAnYAngEmALOAP5OC2e4RUeiAj7IFrHQz8EngD9m5Y4HHSEsJLa9w7RdI\ngfHdpK7Uteu6GTOz7s7zFJsvIp4DileeGUMaTboNafFYImLvDsq4AriigzzllhJ6Edii+lqbmfU8\nHmjTgiLiTEmzgJ3JgqKZmXWBHhIVe1RQBIiIq5tdBzOz3qlFI10VelxQNDOzrtdDGooOimZm1gA9\nJCo6KJqZWd16SEzsvlMyeovfXT+x2VVYYfz43za7CkDr1ANcl3ImXTeh2VVYYeLE1qnLk8/f2+wq\ndC4vCG5d4frrr2t2FVYYP2F8s6sAtE49wHUpZ9J1k5pdhRVaKUCXrmVq+UgaJWlmtqn7FEk7dJB/\nL0lTJS2S9ISkY6q5noOimZnVr9pGYo6GoqQRwIWkPWyHkDZmmFxpqz9JGwF/JO2CtC3wM+DXkvbN\nexsOimZm1qpGA5dFxLiImAGcBCygaNGWEl8GnomI0yLi8Yi4GLguKycXB0UzM6ubEFIVrw6aipL6\nAduzcu9bIiKAW4FdKpy2c3a82OR28q/Co087zxoATzz5eF2FzJs/jwcffKDuyqz9ztU7ztRRXebN\n4/7776+7nJ5SD+iZdXl70ZK6zp8/bx4PPDCt7nqob/2/s8+bN49pDajLnNdn1V3G4iUL6irntTde\nKPxxjbor0wlmzJjecabq8g8E+gKzS9JnA5tXOGdwhfzrSFo9It7u6KJKgdcaTdKRwDXNroeZ9ThH\nRcS1za5EgaQPkLbb61/D6W8DH87WsS4t932kfWx3iYh/FKWfB+wREau0/iQ9DlwZEecVpR1Ies7Y\nP09QdEux80wGjiLt3rGouVUxsx5gDWAj0ndLy4iI5yRtSWrZVWtuuYBYOAYsAwaVpA8CXqpwzksV\n8s/PExDBQbHTRMQrQMv8NmdmPcI9za5AOVlgqxTcai1ziaSpwDDS1n5IUvb+5xVOuxc4sCRtvyw9\nFw+0MTOzVnURcIKkoyVtAVxK6qa9CkDSuZKKN4G4FNhE0nmSNpf0FWB4Vk4ubimamVlLioiJ2ZzE\ns0jdoA+QNoCfk2UZDGxYlH+WpIOAnwBfA54Hjo+I0hGpFXmgjZmZWcbdp2ZmZhkHRTMzs4yDYjcl\n6QOStml2PVqZpKb++5a0qaRtm1kHM6uOg2I3JGld4H5gs2bXBUDSeySt0+x6FJO0FXCMpL5Nuv66\npPlkWzXj+uVkw9lbjqS1m12HYs34NyPpUEkbdPV1bVUOit1TH9KiuDOaXZHsf+QHgQ83uy4F2Zfa\nxcDmEbGsSdV4A+gHvNjsYCRpbUn9IiKaXZdSkk4GvixpcAvU5dOS1o2IZV0ZGCV9ErgaeKurrmmV\nOSh2T2uRNl5Z0OyKAB8AlgAPN7siBVkgXIu0IkaXy7pt300Kiq9EE4d4S/owcBNwZLb2Y8sERknn\nk7YEepK03Fcz63Ii8DvgWknv7uLA2I+0nNmbXXQ9a4eDYjchabCk9bO365KWVOrXxCoVrAMsB5Y2\nuyIFWVBaQvqi6crrri9pQEQsJ/3drEH6bJoiC34nArsBRwKfkrRaKwRGSYcChwMHRMTvgT6SBkra\nuElVWgZMy35eUwiMXXTt1YDXIqK+lditIRwUuwFJA4BfA5dKei8wB1hI1lKU9I7CoJKuGFwiaR1J\nhcV/VyN9+a/ZzIEtkjaU9DlJ78iC0nqkwNglz9IkrQb8FbhB0jtJfzeLaGJQzFqoU4AJWV2+C3ym\n6FgzbQzcHxH3STqE1EqbAtwp6XtNqM8LwGvAROBdwG8g/duR9P5GX0zSPkXPUjcE1m72wDBL/JfQ\nDUTEPOB2UqvsAmAP4FHg/7d35vFWVtX/f39AQcAcySnEAS2VQUSTVEwpS9O0sjIVI0w0nH+aU5o/\nZ000yyEHviH9NMtMcypK8xs4RYKioaj8RIWcvl1TnHAgYH3/WOtwHw73AnHPc+7l3vV5vfbrnGfv\n/Zy9zt7Ps9dea6+1dpc4c+xj+Eu1Cs6cti6Lltj7uQcYEcxmAc4A3gesmkHXiSF1As4BTgWGRZuL\nzvauBwMws3nAt4Et8NNRPon3SU9Ja0laX9ImIfGvK2lHSeuWTRfOlLsDX8VjU54saS9J10vavw7t\nL4bCxN8HeDMWfNcBvwPOAC4CTo+TEOpFk4A3gY/M7Abgp0BXSRPxfb5daqlKlTQEuAr4UWTNBeZR\nOLkqed8AABogSURBVIu+2F5rS/UdDRnRpg1D0lrA2mb2YlyPwlf66wH98UluTZwxVdAZeAv4jJlV\nnytWK7ruwie10bgadx8z+1wzdXuaWel7e6Fa/jGwCb7aPwKf3Gbhk818PKxhJ1yKnFk8jqYF7a4L\ndDazhrgeCPwZN7TZkMbz4FYDVsf3jRYGTf1LHKNOZrYwmM6tZvaFyL8d2BVX7+5hZlMkqd6So6Sv\n46G4bsfH41Azmx9l3wSuD/paPEbLSU9F0j84Tn04HmdabwPbm9krkjrXQqUqqRtwGrAn8AD+bK4P\nXAK8gz+n3XBt0ALcYOwvLW03sXzI2KdtFPLgt6OBFyRda2bPmtm1sWg8DLc8vQZ4Cp/ghL9Ic4EX\naj3Zxovc1czeMrP9JP0SOBaf8HeXNAU3bnkPf666Bk2vStrfzN6pJT1B0wa4y8PTZvZqTGTXAMOB\nrYCr8YXD2ngffYSvyDsBQ2rQ/tbR3gxJ55jZq2b2hKQ9gJvxvjgOP87GgoZ5+ET3Uglj1AvYzMwe\nDIbYKdrdRNL2ZvZY0NQdmA30kjRteY/UqQF9RaYyFZiCL/KmVBhi4ElccitlzzyY7pyqeJir4sy5\np6T3cFXzFPwZHiNpRCHe5oq22x8/wmi2pAvxxdGuwI74gmkPPJbnv6OsE/4e3YAz7EQdkEyxDSJe\nnvuAO4DbzWyR60Uwxs545PdBwB1m9lLcV8qKPywYTwMmSfq9mb1mZodIGoczoEnARHyVW2E63fAJ\n+M8lMcS++GTxd9xisMHMGkKavjyq/Qlffc/Fpeu5+N5aNzN7s4Xt98f/8w34f1x0LLqZ/V3St3CJ\ncV/gCDMr1bJQftDr48Djkkab2b2xt/qOpL8C70u6BhgK7Aacjktqhj9nZdK2MzCpYtFpZgvM7EVJ\nNwPbAftI2tvMxsctc/H9vTKe5Uvwd+dUuVHU2wBmNjek6H2Ao/BDaY/Bx+9s4Dv41sWKtnsazvQm\nSrrKzN4KFfECXNvyNnAi/t97xHV3oIuZPbqi7SZWAGaWqQ0loBfwAq666VRVpsL3I4GHcP+mTUuk\nZwBuxXkjsF/kdSqUj8P3N4cBq9Wpj/rhksSPgQHV/YOrom4DHgS+WyjvVKP2NwKeBs5voqw4RoNw\no6g7gJ4l98lXcOni/+PneO5ZKLsmyl4DPh15q+DS7OYl0/V/cVXy+YXxWbVQ/nXgkeinC4ETcO3H\nb0ug5Whcs7FTM+VnRT+NwU9pB1/gfaaF7Y6Od+iQSn9XnkVcrX4OvrAcDazezG/U5NnNtBzj1doE\nZKoaEDgQuB/4WCHvU5E/Bvh+IX8Urmq6DlilBFo2w49euaj696sY4824OncEsEbJ/bNOLAYubqKs\nK74HC76fdytuoHRMjWnYAz/sdSN8P7HCqA8OZnwKMCjyB+J7RjeVPbEBY3HpdApwN/DFyN8ZP3+u\nQlPnOj3Lw4HngfExZuc1wxh3BM4EZgbdVxbKVAM6hO/t/hI4KfI+H31yD76wWz/y96kwpuq2V4QW\n4FDgJWIxUlXWLT5XwxcPf8Wl9x71GJ9MzYxZaxOQqWpA4PBgMFvE9fCYVJ6Pl2Y+8MtC/UMpSVLE\nVaZ34yqcSt6G+D7ISGDvQv6N+Cp8WC0msqXQtCUuSexayNspGNET+Iq7ItFuANwb/bdmDWk4Ani7\ncD08mNEMYDIurd0JfCLK+wOfLLFPusTnKPyQ1cG4e8N4YLco61bn57gzru24GrfI/VHQ1CRjjOvu\nLC5p12wRgUt8E3GJejCuabgqmNAzMWZbltDutcDlhWvhi6rLY3yOjvyuuKQ6ExhWz7HKVDVmrU1A\npiWkrl3wCB9/wPfE3gUuJlQ+wN74PsSQOtB1Ja76q0xiBwC/xSPFvIJbx51aqH8t0KckWioT/w64\nRWmF8X0vJtsJuMXib/CFw85R/vEKc2ph+9sCJ8b39fCFywu4pPE+cAGhZsMXBm/QQrXbMujZkMLC\nIPLWjXE5ELfCnRzP0ecLdUpbsDRB4xqEehv3/RvdBGMsMsFVyqCz8rvAfwO/xhd7ZxXKO+P7sX8o\noQ9uxrUEFalwHM6cp0fZwso7hO/Df7Ve45OpmTFrbQI6esJX0efjBhujYsW4H42+W7tSUKcAX8D3\ns0phPlW0HY9bbJ6F712+Hozyc7hxwI+ClrL3pQYFw/1YXN8dTOe5CmMG+kXZBrhUfVIN298WN9C5\nIK47BU1X4CrtHXDL3Er9AdEvO5TUH5vhbjdv4IuWnQhtAS7F3hrft8NVqbdTkOpLHqvDgLXie4Xx\nVfbPKozxEeDcyOsDXFI2LXH9RXy74Z/AyZHXNT73x6W0DVrKkKuehSNwS9/7o93J+DZD5Vm+OMp7\nVv1G3RYvmRZPaX3aipAfK3Qfvkrtje9nfBYYZWZ3VXzNqm4biktqc0qgp3f8/sbA78zs8vDD+3JU\nGQE8YuF3KOlVfKXbIlP1ZdC0LT6JXm5m7wKY2b6SDsWNRSaY2cyq2/6Fu2LUqv1JwGVmdka0vxB3\nKZjajO/awbjl7axa0NAEtsIXKzOAzYGTgU9IuhRoALaQtJOZTZI0Epfuh0uaaGalxcuVxw+9FjhJ\n0s5mNkceYWh+PMtvSboItyrdQ1JPYC9KiFHbFC14fz0IfBffA8YWd0dpAD604Eor2O4o4LPx3txm\nZmMkzcXtAu7DfWffLzwzH+Fq/8WsoVtCQ6KFaG2u3FET0BdXu51B7K3gxgbvECoUFlcn9cZX2W9T\nsLisIT3b4ozkKfxFfRM4PMrWoLD6LdxzGS6FNGkxVyOa5gIX/gf3nIerNjeuQfvbRH9fVJW/F+54\nD4ur/zbFV/5zyhijKhoOwCf4K3CrxhHxv8fgC5W7aJSC+uL+i2U/0/vje3PPx7O0buRX1JcVifFj\neNjChcBvCvfXUmVaTUvPwpiOjbbH4tL0zsCjwDUtbPMyfIF4HTANZ7IX0IwRHG4lPQU4p+yxyfQf\njGNrE9ARE77381y8iEUjltVxS7Wjq+qfjVtSPglsWwI9/YP5nIWrj3rghiINwEZRpzj5r4lbpL5B\nqC1LoGmzYM7nx3VlQj0W+HoT9QfhRhNvAANr0L6izz/A1cUVVeCZuBps66r6R+AO1k+UMUaFdjoX\nvg/HpehxMW4b4qr3BwhjDepoyg9sHYzoB/hi6SVgnSirXuDNBm4p5NWUziZoebnAGDfFXZpmxTP+\nDHBzcexXoL2LcJX2FoW8X0e7mxV/F7egHhKM8+6WtJup9qnVCeioCbd8m4SbYq8XeQOCEexbVfcr\nuI/VJiXQ8Ql81XxTVf6gYJR7VOVfgPvBzawF82mGJuFWuK8CVxXyT8cl2M9W1f8ers6cSA2ZNB4J\nZwLuTjA4JtgGqvbncDXuTrjE1mIJtQk6KhJXZVItLqSGxX//f0Dfej2/VfQVDcVOxa2kh0a/zapm\njPHs/7mp+0umpSi9dsKD2Q8grE5XlBbc+G0h8KOq/F3jeRlYyNsIX2zdD4wtow8ytfAZam0COnLC\n1S2P4Q7LA+OlvaJQXnzBS/Mtw1es0/HV62qRNzSY4g5VdY/Go8SUauiDq2yPDNp+GhNcA/ClZuoP\nJRYXLWy3VzCao3D/sXVjYn0ZV6XuFfWWWNU3lVcDevrg1rRX4FJq9ybqDIvnaBwlLVSaoe0wXMXd\ns5A3CLd43T5on1xkjFGnU1Pf60TL7CIttRg/XHPyK1xCP45G39WfxH9fq6r+/ni84Jr2QabapFYn\noKOkmGwPiBdiu0L+j3GV2zvAuEJ+2Y7eYnGJ4xF8/2XrmEBeAy5t5t4uJdG0Hm5oNBRX4a4SzGk6\nvhL/QtQrqhBrtljA996ewH0uL6ZRZbsmflDvDNz6t5JfuroLdzJfiO8n34i7gRxPuJwU6g3H3R1u\npSSVdlV7hwVdz+J7c8cXym4Axsf3rYGHgRcpycKyFrS0oO3KIrIHvk86GV+knIFrNQZHeaem3ul6\nPEOZ/sMxbW0COkLC9+xm4Zvq/4MbQXyqUH5BTHZn0WjOXhpTxI81uhJ3+fhBIX8KLhG9RsHooGwG\nXeijZ4MxLwwm9Gk8UPMxuMR4baF+TSXnYIhv4oY6axTyv4b7jnbH1bOTcHVZPRnjFTT6sp2KO8Q3\n4PtYXy7UG4ar5TYqmR7h7g3Tg46R+P7hnbiKeztc7bx9YWyfwy142wUtwEH4gvYRPDLOgcH4xsYz\n/D6h1aCEaFOZykutTkB7T7gTdSVUWg/gS8F0dqyq9xPc8OaHxL5HSfRsG5PH7bghwLwqxngvjdH7\n67KKxfd15uJ+j9vhEvVbuHOzcFXqsbgU9/PCfTVhjLjhw/0UwotF/qnRF/cDn4nxm4BbfX61jv1z\nGvBwVd7UGMepuIHPV3HJui4hwnCH993i2f4FrmYeGc/PnOi3owv1e7UXWvDtg1nxfP4c1yDMD4bY\nE/gZ7qd6BFWWt5nafmp1Atp7ihdjAotbb/4h8ocTKsHIvyRWmaeU8RIF83mfxZ3QrwyGXJSOJuAq\npp1rxXiWQtMWeNSeMVX5Z8SEtklcrx6M8VEKZvw1omFr3HBoKI0S4Ch8wXBUTK734MY03XGp9Y9l\nMCDc8OlbuOSxQyF/BnBKfP8Fvje2G24A9FDQtEEdnufF9gOB3XGL35sK+cNx9fMS9FBbt4u604Kf\nZPEaHrChwvA2jvwP8ahKCmb513h+Vv1P28nUeqnVCWjvCbeMfJ7YR4zJfiEeK3Mybt4/slD/PErw\nKYsX93UKZvCRfzMePOAZ3Ll438ifSGFPpMT+2YvGPbOiFeBhuEp5cxqtLlfHFwwPABvWkIZD8JV+\nceHSiwijhjt634dLZR/HJctNS+iLAfGsTMcZ8tM0ulb8n5hof1+ZlKvu/XjJ4zSYJsLlBQPYHZda\ni+4FFYZRhvFR3WmJ3+6BL5COK+RVns01Y4zmxfPUAw9A/hyFMHuZ2n5qdQLae8L97R6Ol+PWYABf\niRdqPTww8AQiSn+JdGwaTPhOYJfIOw1XW/4wmNAzuFqod5TfR8Hvqsb0fBxfbW+I7wm9jEuta0XZ\nv4DzCvWLjHHtGtMyBF/l719sK75XJMfDo/9KUQPSqEK+GDfb3xuP1TkVd/LeFlcpz6HAkKnDiRe4\ntLMQ36sbyZKq/1WCGf0TP99zsTFrL7TgUvxbNO4VVp+isVGM16/jem08OlWp45Opxs9YaxPQEVIw\nxgNwJ/zfVpWdiu+VlX4WIX7CxB+DMf5XTBxfLJT3jgmnpkctNUHHNrjK7148nBzAt2OiG8eS/olL\nBI+uMT29oi/upBlfUPyA2VsoHOlVw/abk+IPD0a5TVyfhO9n1kxKXk76DsX9IL+Nx519GFfh9qUx\n0HUnXJ3bADzYHmnBI/E0AKc3UVZ5Rs/Do0J1a6o8U9tPnUiUDjN70cxuwaWhbpK6FIrXx6WzznWg\n4zncnL8bbqk42szulWNV/PSNabiFLJJUaxok9cUnsvvxlf4BQduNuOS6H662vbJAtxU/aw0zexmX\nQPYCzpO0TYHeNSSNxuNlnmMRf7XG6Izv4XaVNKSQPwvfA141rqfhhhz9SqBhaXgANxD7J34o8Am4\nCnkMcIukwfie9P04s5rWTmkxfC93H0l9KplV78nawCQz+0DSotjSZT27iRLQ2ly5IyVcQnoLX/Ef\nQmOczP51pqMPbjgynsXPJTwX38ereVSW+P11cEnn8qr8YgiwA/DFw08pSXXbDG2d8f3ff+Nq5LF4\nQOm78T287UpuvyLF34Mb/qyOSyUXV9V7CHioDv3RqerzWNyFp1dcfzL6ajp+XNXvCUOgwm/Uyg+x\nLdEyNNr6BVWnw+DbIc/EO/4E8H3qfI5lphqMcWsT0NFSvFQz8biMEyg5cPRS6KhMwn/C3SBOweN8\nljb5x6JgJu6g36mqrGi0MAxfkV9fPfHUoV8GA7fFpPYg7kpTF+YcYzKeRiOnnxTKKudJ7lIPeqja\nq8Tjvz6F75utjUtq46Lsy7GAuLG90xJtHIUb1PwlGHQ/4BvA3+OdPhD4JiXbCWQqJ1UmoUQdIWkd\nXCX2kZm91Yp0bImHmtsRn1x2MrPHSmzvYHw/qIuZWVNHY0nqHrTsgDOkoWb2z7JoaobOpo6Dqlfb\nWxKHNQPDzeyByG/qGLEy2j8I7/sheAD6qWZ2dZSNwxcN6+FuRUeZ2dwo62pxDJMkWQ0mlrZESxVd\nlYABP8X3o7vhrkJPmNmoWraVqD+SKXZwSPoU7g5xuplNL7mtnXGLykPM7LZm6hyLW4EOlbSGmb1T\nJk3N0LBoIi1jUl2O9rfA91SFW+A+XKd2L8ElnL/h50HuigefuBcPDtAPdyUaT6iaq/umhgyxzdCy\nFBrXxv1W1wNeMbOGyG+1RVWi5chDhjs4zGyGpG+Y2b/r0NxsPMbrcEmPmtlsWGLy2hSYFkYKZRi1\nLBPFibTeDDHanCnpOFyKv1TSCWb2tzLblHQivs+9Ly7xzJe0Mc6YzsXdDL4l6Qn8/Mx5cZ9q3V9t\niZalwfzg4jn4PmaFdiVDXLmR1qcJ6sQQMbNX8JMv9qRg5Rmq1O6SLsQtCq82s/mtwZDaCswthU/G\njY5eLaudsDzugVveXmRmjwILYnJ/CTc4+iHwtVB//xDYXdJ+QWfNxqgt0bKiaAs0JFqGZIqJeuMO\n3C3kIOA2SddLuhqPw3oY8DUzm9GaBLYVmNmzeESbf5TYhuEBE3bEA0wU8zGzt3H/zKfwgALP4hJ8\nr/ZMS6LjIplioq4ws4Vmdh1uRfkUbvnaDzdlH2Jmj7cmfW0NFdVgyXgHt6bcLtpcJO2ElPYqbswy\n0NxPc0TF4KWd05LogMg9xUSrwMwmSzow91/aBIpO6b8xs+ehSaf0R+L7pCgvwyK2LdGS6IBISTHR\nmlg0iZURPSexfDCz93A/1R2BMyVtHvkW+73r4Ycdfz2MW46T1K0MJtSWaEl0TKRLRiKRAEDSUbjv\n3UP4eZsTgK2AM/FgAtfhoQAfsJJ9R9sSLYmOhWSKiUQCaFtO6W2JlkTHQjLFRCKxGNqSU3pboiXR\nMZBMMZFILBOtEdmnObQlWhLtD8kUE4lEIpEIpPVpIpFIJBKBZIqJRCKRSASSKSYSiUQiEUimmEgk\nEolEIJliIpFIJBKBZIqJRCKRSASSKSYSiUQiEUimmEgkEolEIJliIrEMSNpE0kJJA+J6N0kLJK3R\nCrRMkHRZC+5/UdJxtaQpkWhPSKaYWCkhaVwwqgWSPpL0nKQzJZX1TBdDPz0MbGhm7yzPjS1lZIlE\non7IQ4YTKzP+CIwAVgO+BFwNfASMrq4YzNJaEDNz0XmPZjYfaFjB30kkEm0YKSkmVmZ8ZGavm9lL\nZjYGuA/4CoCkEZLmSNpX0nTgQ2DjKBsp6WlJH8TnkcUflbSjpKlRPhnYjoKkGOrThUX1qaRdQiKc\nK+lNSX+UtKakccBuwPEFybZ33NNP0nhJ70r6H0k3SFq38JvdI+9dSa9IOnF5OiX+8+Sg/3VJty2l\n7gmSpkl6T9I/JP1MUo9CeW9Jd8V/ek/Sk5L2irK1JN0kqUHS+5JmSPrO8tCYSLRVJFNMtCd8CHSJ\n74YfOXQKcBjQF2iQNAw4G/gBfmjt6cC5kr4NEAzhbuApYFDUvbSJtopMciDOkJ8CPgPsBNwJdAaO\nByYB/wWsD2wIvCRpTeC/gceinT3x45FuKbRxKbArsC9+tuDuUbdZSNoH+B3we2Bg3PO3pdyyADgW\n2AYYDgwFLi6UX4336RCgH3Aq8F6UnY/34Z7xeSTwr6XRl0i0daT6NNEuIGkPfHK+vJC9CnCkmT1V\nqHc28H0zuzOyZkvqC3wPuBEYhqtKR5rZPOAZSRvjzKE5nAxMMbNjC3kzCm3OA943s9cLeccAU83s\nzELeSOAfkrYAXgO+CxxsZhOj/DvAy8voitOBX5nZuYW86c1VNrMrCpf/kHQmcA1wTORtDNxqZk/H\n9axC/Y2Bx83s8cr9y6AtkWjzSKaYWJmxr6R3gVVxRnYTcE6hfF4VQ+wO9AHGSvp5od4qwJz4vhUw\nLRhiBZOWQcdAFpfwlgfbAp8L+ouwoLE7/r8mLyowmyNpBkvHQGDM8hIRi4nT8P+9Bt4XXSWtZmYf\nAlcA10jaE5eGbzOzJ+P2a4DbJG0P3AvcYWbL6qtEok0j1aeJlRl/AQYAWwDdzOy7ZvZBofyDqvqr\nx+dInClVUl9c5bmiqG5nebA6cBdOf5GWLYEH6kGLpE1wVfETwP64avboKO4CYGZjgc2AG3D16RRJ\nR0fZn4DewGW4Wvg+SUsYOSUSKxOSKSZWZsw1sxfN7GUzW7isymbWALwK9DGzF6rS7Kj2DDBAUpfC\nrctimNOAzy+lfB6+v1jEVJwZz26Clg+A54H5wODKDZLWBj7ZQlqK2B4/aPwkM5tsZjOBT1RXMrNX\nzGyMmX0DZ4CHF8reMLMbzWw4cAJwxHK2nUi0SSRTTHQ0nAX8QNKxkrYMC9ARkk6I8l/hKsyfS9pa\n0t7A95v4HRW+XwR8Oiw3+0vaStIoSetE+SxgcAQBqFiX/gxYB7hZ0g6SNpe0p6TrJcnM5gJjgUsk\nDZXUDxiHG8YsDecAB0k6O+joL+mUZurOBFaVdJykzcLY6HuL/UnpJ5K+KGlTSYNwQ5yno+wcSftJ\n6hP7sl+ulCUSKyuSKSY6FEIdOBI4FJeqJgLfAV6I8rm4tWc/XJo7D7dgXeKnCr/5HG4dOgB4BHfu\n3w+X9MCtSBfgDKNBUm8zew3YBX8H7wlaLgPmFHwpTwYexNWs98b3x5bx/+4Hvhn/4XF8H/DTzdA9\nDTgx/t+TwEH4/mIRnYGrgvbxwLM0qljnARcCf8f7cX78RiKx0kIr7sucSCQSiUT7QkqKiUQikUgE\nkikmEolEIhFIpphIJBKJRCCZYiKRSCQSgWSKiUQikUgEkikmEolEIhFIpphIJBKJRCCZYiKRSCQS\ngWSKiUQikUgEkikmEolEIhFIpphIJBKJROB/AdmsIcUyU/Z1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x67aee96cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalize the matrix\n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8, 4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svm3.1.pkl']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(rand_search_cv, \"svm3.1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.externals import joblib\n",
    "rand_search_cv = joblib.load(\"svm3.1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
